13 citations · 34 across the 7 of their papers we have counts for
9 papers
Reasoning on the Simplex: Geometric Fixed-Point Models
Talgat Daulbaev, Ilya Glazkov, Maxim Rakhuba +1
Looped reasoners spend test-time compute by iterating a weight-tied map, but a small residual does not mean the state is a fixed point when that map lives in unconstrained latent s…
Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking
Daria Frolova, Talgat Daulbaev, Egor Sevriugov +4
Accurate prediction of protein-ligand binding poses is crucial for structure-based drug design, yet existing methods struggle to balance speed, accuracy, and physical plausibility.…
LoTR: Low Tensor Rank Weight Adaptation
Daniel Bershatsky, Daria Cherniuk, Talgat Daulbaev +2
In this paper we generalize and extend an idea of low-rank adaptation (LoRA) of large language models (LLMs) based on Transformer architecture. Widely used LoRA-like methods of fin…
Meta-Solver for Neural Ordinary Differential Equations
Julia Gusak, Alexandr Katrutsa, Talgat Daulbaev +2
A conventional approach to train neural ordinary differential equations (ODEs) is to fix an ODE solver and then learn the neural network's weights to optimize a target loss functio…
Towards Understanding Normalization in Neural ODEs
Julia Gusak, Larisa Markeeva, Talgat Daulbaev +3
Normalization is an important and vastly investigated technique in deep learning. However, its role for Ordinary Differential Equation based networks (neural ODEs) is still poorly…
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva +3
We propose a simple interpolation-based method for the efficient approximation of gradients in neural ODE models. We compare it with the reverse dynamic method (known in the litera…